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A Q-learning approach is often used for navigation in static environments where state space is easy to define. In this paper, a new Q-learning approach is proposed for navigation in dynamic environments by imitating human reasoning. As a model-free method, a Q-learning method does not require the environmental model in advance. The state space and the reward function in the proposed approach are defined according to human perception and evaluation, respectively. Specifically, approximate regions instead of accurate measurements are used to define states. Moreover, due to the limitation of robot dynamics, actions for each state are calculated by introducing a dynamic window that takes robot dynamics into account. The conducted tests show that the obstacle avoidance rate of the proposed approach can reach 90.5% after training, and the robot can always operate below the dynamics limitation.

A new minimally invasive surgical (MIS) robot consisting of a spherical remote center motion (RCM) mechanism with modular design is proposed. A multi-objective dimensional synthesis model is presented to obtain the excellent performance indices. There are four objectives: a global kinematic index, a compactness index, a global comprehensive stiffness index, and a global dynamic index. Other indices characterizing the design requirement, such as workspace, mechanical parameter, and mass, are chosen as constraints. A new decoupled mechanism is raised to solve the coupled motion between the linear platform and the four degrees of freedom (DoF) of surgical instrument as a result of post-driving motors. Another new mechanical decoupled method is proposed to eliminate the coupled motion between the wrist and the forceps, enhance the dexterity of surgical instrument, and improve the independence of each motor. Then, a 7-DoF MIS robotic prototype based on optimization results has been built up. Experiment results validate the effectiveness of the two mechanical decoupled methods. The position change of the RCM point, accuracy, and repeatability of the MIS robot meet the requirements of MIS. Successful animal experiments validate the effectiveness of the novel MIS robot.

Although image-based visual servoing (IBVS) provides good performance in many dual-arm manipulation applications, it reveals some fatal limitations when dealing with a large position and orientation uncertainty. The object features may leave the camera's field of view, and the dual-arm robot may not converge to their goal configurations. In this paper, a novel vision-based control strategy is presented to resolve these limitations. A visual path planning method for dual-arm end-effector features is proposed to regulate the large initial poses to the pre-alignment poses. Then, the visual constraints between the position and orientation of two objects are established, and the sequenced subtasks are performed to attain the pose alignment of two objects by using a multi-tasks IBVS method. The proposed strategy has been implemented on a MOTOMAN robot to perform the alignment tasks of plug–socket and cup–lid, and results indicate that the plug and socket with the large initial pose errors 145.4 mm, 43.8○ (the average errors of three axes) are successfully aligned with the allowed pose alignment errors 3.1 mm, 1.1○, and the cup and lid with the large initial pose errors 131.7 mm, 20.4○ are aligned with the allowed pose alignment errors −2.7 mm, −0.8○.

This paper investigates the vision-based pose stabilization of an electrically driven nonholonomic mobile robot with parametric uncertainties in robot kinematics, robot dynamics, and actuator dynamics. A robust adaptive visual stabilizing controller is proposed with the utilization of adaptive control, backstepping, and dynamic surface control techniques. For the controller design, the idea of backstepping is used and the adaptive control approach is adopted to deal with all uncertainties. We also apply the dynamic surface control method to avoid the repeated differentiations of virtual controllers existing in the backstepping design procedure such that the control development is easier to be implemented. Moreover, to attenuate the effect of disturbances on control performance, smooth robust compensators are exploited. It is proved that all signals in the closed-loop system can be guaranteed to be uniformly ultimately bounded. Finally, simulation results are presented to illustrate the performance of the proposed controller.

This paper deals with sensor-based motion planning method for a robot arm manipulator operating among unknown obstacles of arbitrary shape. It can be applied to online collision avoidance with no prior knowledge of the obstacles. Infrared sensors are used to build a description of the robot's surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The point automation is confined on some planes with square grids in the C-space. A path-searching algorithm based on square grids is used to guide the automation maneuvering around the C-obstacles on the selected planes. To avoid the construction of the C-obstacle surface, the robot geometry model is expanded, and the static collision detection method is used. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of experiments.

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